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arXiv · 2512.12654

Modeling Authorial Style in Urdu Novels Using Character Interaction Graphs and Graph Neural Networks

Hassan Mujtaba·Hamza Naveed·Hanzlah Munir
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Abstract

Authorship analysis has traditionally focused on lexical and stylistic cues within text, while higher-level narrative structure remains underexplored, particularly for low-resource languages such as Urdu. This work proposes a graph-based framework that models Urdu novels as character interaction networks to examine whether authorial style can be inferred from narrative structure alone. Each novel is represented as a graph where nodes correspond to characters and edges denote their co-occurrence within narrative proximity. We systematically compare multiple graph representations, including global structural features, node-level semantic summaries, unsupervised graph embeddings, and supervised graph neural networks. Experiments on a dataset of 52 Urdu novels written by seven authors show that learned graph representations substantially outperform hand-crafted and unsupervised baselines, achieving up to 0.857 accuracy under a strict author-aware evaluation protocol.

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Hassan Mujtaba, Hamza Naveed, Hanzlah Munir. 2025-12-14. Modeling Authorial Style in Urdu Novels Using Character Interaction Graphs and Graph Neural Networks. https://arxiv.org/abs/2512.12654

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